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Ananya Majumdar

Publications and source records attributed to Ananya Majumdar.

3 recordsLinked to original sources

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding

Small molecule double-stranded DNA intercalators have significant potential for therapeutic applications. However, screening for and confirming a drug candidate's intercalative behavior remains labor-intensive and costly. To address this, we investigated the sequence and biophysical parameters that affect the performance of electrochemical DNA hairpin sensors for streamlined identification of structural intercalators. These sensors utilize oligonucleotide (oligo) sequences that form hairpins upon intercalator binding. The 3prime end of the oligo is modified with alkylthiol linkers for gold electrode surface monolayer self-assembly, while the 5prime end carries a methylene blue redox reporter. Hairpin formation enhances electron transfer between methylene blue and the gold electrode, which can be detected via voltammetry. We tested seven hairpin structures varying in stem length and sequence. Our optimal oligo, HP4, features a four-base-pair stem and responds to five DNA intercalators over a broad detection range, with EC50 in close agreement with published affinity (KD) values for these interactions. We further demonstrate HP4s ability to discriminate intercalator binding from a series of minor groove binders through significant differences in signal gain upon incubation. Altogether, our strategy establishes a platform for identifying intercalative compounds that should support the development of DNA-targeting therapeutics.

physics.bio-ph↗

Reduction in turbulence-induced non-linear dynamic vibration using tuned liquid damper (TLD)

In the present research work, an attempt is made to develop a coupled non-linear turbulence-structure-damper model in a finite volume-finite difference (FV-FD) framework. Tuned liquid damper (TLD) is used as the additional damping system along with inherent structural damping. Real-time simulation of flow-excited bridge box girder or chimney section and the vibration reduction using TLD can be performed using the developed model. The turbulent flow field around a structure is modeled using an OpenFOAM transient PISO solver, and the time-varying drag force is calculated. This force perturbs the structure, causing the sloshing phenomena of the attached TLD, modeled using shallow depth approximation, damping the flow-induced vibration of the structure. The structural motion with and without the attached TLD is modeled involving the FD-based Newmark-Beta method using in-house MATLAB codes. The TLD is tuned with the vortex-shedding frequency of the low-Reynolds number flows, and it is found to be reducing the structural excitation significantly. On the other hand, the high-Reynolds number turbulent flow exhibits a broadband excitation, for which by tuning the TLD with few frequencies obtained through investigations, a good reduction in vibration is observed.

physics.flu-dyn↗

Sensitivity mapping of TBL wall-pressure spectra with CFD turbulence models for wind tunnel test result prediction

In the present work, an attempt is made to map the sensitivity of the existing zero pressure gradient (ZPG) turbulent boundary layer (TBL) wall-pressure spectrum models with different TBL parameters, and eventually, with different Reynolds Averaged Navier Stokes (RANS) turbulence models, simulated in OpenFOAM and ANSYS Fluent solvers. This study will help future researchers to choose a particular RANS turbulence model vis-à-vis a particular wall-spectrum model in order to obtain a reasonably accurate wind tunnel result predicting capability. First, the best-predicting pressure spectrum models are selected by comparing them with wind tunnel test data. Next, considering the experimental TBL parameters as benchmarks, errors in RANS-produced data are estimated. Furthermore, wall-pressure spectra are calculated following semi-empirical spectrum models using TBL parameter feed obtained from experiments and computational fluid dynamics (CFD) simulations. Finally, sensitivity mapping is performed between spectrum models and the RANS models, with different normalized wall-normal distances (y+).

physics.flu-dyn↗